Aims and objectives
Variations in breast density in imaging are caused by varying proportions of fat and fibro-glandular tissue.
Breast density is an independent marker of breast cancer risk and therefore a number of techniques have been developed to measure breast density using different imaging modalities (1).
The aim of this research was to compare a fully automated technique of producing volumetric measurements of fat and fibroglandular breast tissue from segmented magnetic resonance imaging (MRI) and to compare with the well-established,
observer-dependent Breast Imaging Reporting and Data Systems...
Methods and materials
This was a prospective inter-method comparison study.
The study design was a prospective analysis of volumetric breast density obtained from breast MRI scans compared with mammographic breast density using BIRADS.
Ethical approval for the study was obtained from the local Research Ethics Committee.
40 women undergoing mammography and dynamic breast MRI as part of their clinical management were recruited.
Fat-water separated MR images derived from a 2 point Dixon technique using phase-sensitive reconstruction and atlas based segmentation were obtained before and after the administration of...
Results
The mean unenhanced breast percentage of fibro-glandular tissue measured on MRIwas 0.31 ± 0.22 (mean ± SD) for the left and 0.29 ± 0.21 for the right.
The mean density on the contrast-enhanced images was 0.32 ± 0.19 for the left and 0.32 ± 0.2 for right.
There was “almost perfect” correlation between the quantification pre and post-contrast breast fibro-glandular tissue quantification: Spearman correlation rho=0.98 (95% confidence intervals (CI): 0.97-0.99) for the left and rho=0.99 (CI: 0.98-0.99) for the right (Figure 3).
For each of...
Conclusion
Automated breast fat density measurement using MR correlates strongly with the current mammographic standard BIRADS.
Results for percentage fibro-glandular component on unenhanced breast MR correlate very closely with post-contrast MR.
Breast density measurements derived from automated segmentation of unenhanced breast MRI could be used instead of mammographic measurements for assessing breast cancer risk.
Personal information
Elia Petridou FRCR,
Department of Radiology,
Norfolk and Norwich University hospitals,
Norwich,
Norfolk,
United Kingdom;
[email protected]
Minnie Kibiro FRCR,formerly Department of Radiology,
Norfolk and Norwich University hospitals,
Norwich,
Norfolk,
United Kingdom,
currently Department of Radiology,
University of Toronto,
Toronto,
Canada;
[email protected]
Christina Gladwell MBBS,
Department of Radiology,
Norfolk and Norwich University hospitals,
Norwich,
Norfolk,
United Kingdom;
[email protected]
Paul Malcolm FRCR,
Department of Radiology,
Norfolk and Norwich University hospitals,
Norwich,
Norfolk,
United Kingdom;
[email protected]
Arne Juette FRCR,
Department of Radiology,
Norfolk and Norwich University hospitals,
Norwich,
United...
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